def test_clarifai_api_extractor_batch():
stim = ImageStim(join(IMAGE_DIR, 'apple.jpg'))
stim2 = ImageStim(join(IMAGE_DIR, 'obama.jpg'))
ext = ClarifaiAPIImageExtractor()
results = ext.transform([stim, stim2])
results = merge_results(results)
assert results['ClarifaiAPIImageExtractor#apple'][0] > 0.5 or \
results['ClarifaiAPIImageExtractor#apple'][1] > 0.5
def test_clarifai_api_extractor_large():
default = config.get_option('allow_large_jobs')
default_large = config.get_option('large_job')
config.set_option('allow_large_jobs', False)
config.set_option('large_job', 1)
ext = ClarifaiAPIImageExtractor()
images = [ImageStim(join(IMAGE_DIR, 'apple.jpg')),
ImageStim(join(IMAGE_DIR, 'obama.jpg'))]
with pytest.raises(ValueError):
merge_results(ext.transform(images))
config.set_option('allow_large_jobs', True)
results = merge_results(ext.transform(images))
assert 'ClarifaiAPIImageExtractor#apple' in results.columns
assert results.shape == (2, 49)
config.set_option('allow_large_jobs', default)
config.set_option('large_job', default_large)
def test_clarifai_api_extractor():
stim = ImageStim(join(IMAGE_DIR, 'apple.jpg'))
ext = ClarifaiAPIImageExtractor()
assert ext.validate_keys()
result = ext.transform(stim).to_df()
assert result['apple'][0] > 0.5
assert result.ix[:, 5][0] > 0.0
result = ClarifaiAPIImageExtractor(max_concepts=5).transform(stim).to_df()
assert result.shape == (1, 9)
result = ClarifaiAPIImageExtractor(min_value=0.9).transform(stim).to_df(
object_id=False)
assert all(np.isnan(d) or d > 0.9 for d in result.values[0, 3:])
concepts = ['cat', 'dog']
result = ClarifaiAPIImageExtractor(
select_concepts=concepts).transform(stim)
result = result.to_df()
assert result.shape == (1, 6)
assert 'cat' in result.columns and 'dog' in result.columns
url = 'https://via.placeholder.com/350x150'
stim = ImageStim(url=url)
result = ClarifaiAPIImageExtractor(max_concepts=5).transform(stim).to_df()
assert result.shape == (1, 9)
assert result['graphic'][0] > 0.8
ext = ClarifaiAPIImageExtractor(api_key='nogood')
assert not ext.validate_keys()
stim = ImageStim(join(IMAGE_DIR, 'obama.jpg'))
result = ClarifaiAPIImageExtractor(model='face').transform(stim).to_df()
keys_to_check = ['top_row', 'left_col', 'bottom_row', 'right_col']
assert [k not in result.keys() for k in keys_to_check]
assert all([result[k][0] != np.nan for k in result if k in keys_to_check])
stim = ImageStim(join(IMAGE_DIR, 'aspect_ratio_fail.jpg'))
result = ClarifaiAPIImageExtractor(model='face').transform(stim).to_df()
assert [k in result.keys() for k in keys_to_check]
def test_clarifai_api_extractor():
stim = ImageStim(join(IMAGE_DIR, 'apple.jpg'))
ext = ClarifaiAPIImageExtractor()
assert ext.validate_keys()
result = ext.transform(stim).to_df()
assert result['apple'][0] > 0.5
assert result.ix[:, 5][0] > 0.0
result = ClarifaiAPIImageExtractor(max_concepts=5).transform(stim).to_df()
assert result.shape == (1, 9)
result = ClarifaiAPIImageExtractor(min_value=0.9).transform(stim).to_df(
object_id=False)
assert all(np.isnan(d) or d > 0.9 for d in result.values[0, 3:])
concepts = ['cat', 'dog']
result = ClarifaiAPIImageExtractor(
select_concepts=concepts).transform(stim)
result = result.to_df()
assert result.shape == (1, 6)
assert 'cat' in result.columns and 'dog' in result.columns
url = 'https://via.placeholder.com/350x150'
stim = ImageStim(url=url)
result = ClarifaiAPIImageExtractor(max_concepts=5).transform(stim).to_df()
assert result.shape == (1, 9)
assert result['graphic'][0] > 0.8
ext = ClarifaiAPIImageExtractor(api_key='nogood')
assert not ext.validate_keys()